Discovery Proteomics Core: Summary The Discovery Proteomics Core (DPC) uses a full range of protein profiling technologies that are very positively leveraged by complementary biophysics and lipidomics analyses. Proteins whose expression or posttranslational modifications (PTMs) are altered by exposure to drugs of abuse in animal or cell-based systems will be identified by ?bottom-up? MS-based technologies that will include iTRAQ, SILAC/SILAM, Label Free Quantitation (LFQ), Multi-dimensional Protein Identification Technology (MudPIT), and by coupling of LFQ to the data independent acquisition technology of SWATH. In addition, the Core will use Top-down/Middle- down proteomics methodologies to characterize intact proteoforms and their PTMs. Innovative sample preparation approaches will be used to reach deeper into the neural proteome with laser capture microscopy (LCM), fluorescence-based cytometry methods (FACS and FANS), and immuno-affinity methods in conjunction with transgenic and viral approaches that will enable analysis of cell type- and organelle-specific proteomes. Proteome expression will be validated by MRM/SWATH assays carried out in collaboration with the Targeted Proteomics Core (TPC). The availability of several biophysical technologies including isothermal microcalorimetry (ITC), static and dynamic light scattering, circular dichroism (CD), surface plasmon resonance (SPR), stopped-flow (SF), and asymmetric flow field-flow-fractionation (AFFF) will extend protein profiling analyses into the functional domain by quantitatively characterizing the thermodynamics and kinetics that underlie protein:protein and protein:ligand interactions of interest. Among the latter are phosphoinositides that have the ability to control virtually every aspect of neuronal function via their interactions with and modulation of the activities of neuronal proteins. Biophysical and phosphoinositide analyses, combined with proteome level analyses will provide an increasingly biological systems level approach. All protein profiling data will be stored in the Yale Protein Expression Database (YPED) and its Repository will be used to disseminate these data. Further, together with the Bioinformatics and Biostatistics Core (BBC), the DPC will use RNA-sequencing to construct experiment- and cell-type specific MS/MS protein reference databases that will be complemented by a PTMome database to increase peptide and protein identification rates. By taking a holistic approach, the DPC will provide Center investigators with the broad range of tools and training needed to identify, and then to understand why certain proteins and their phosphoinositide effectors are differentially expressed following exposure to psychostimulants and psychotropic drugs.